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Autonomic machine learning platform

Author

Listed:
  • Lee, Keon Myung
  • Yoo, Jaesoo
  • Kim, Sang-Wook
  • Lee, Jee-Hyong
  • Hong, Jiman

Abstract

Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning.

Suggested Citation

  • Lee, Keon Myung & Yoo, Jaesoo & Kim, Sang-Wook & Lee, Jee-Hyong & Hong, Jiman, 2019. "Autonomic machine learning platform," International Journal of Information Management, Elsevier, vol. 49(C), pages 491-501.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:491-501
    DOI: 10.1016/j.ijinfomgt.2019.07.003
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    Cited by:

    1. Wang, Kai & Gong, Haoran & Wang, Gongda & Yang, Xin & Xue, Haiteng & Du, Feng & Wang, Zhie, 2024. "N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms," Energy, Elsevier, vol. 290(C).
    2. Christian Neunzig & Dennis Möllensiep & Bernd Kuhlenkötter & Matthias Möller, 2024. "ML Pro: digital assistance system for interactive machine learning in production," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3479-3499, October.

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